mirror of https://github.com/minio/minio.git
307 lines
15 KiB
Markdown
307 lines
15 KiB
Markdown
# **Disaggregated HDP Spark and Hive with MinIO**
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## **1. Cloud-native Architecture**
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Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.
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All access to MinIO object storage is via S3/SQL SELECT API. In addition to the compute nodes, MinIO containers are also managed by Kubernetes as stateful containers with local storage (JBOD/JBOF) mapped as persistent local volumes. This architecture enables multi-tenant MinIO, allowing isolation of data between customers.
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MinIO also supports multi-cluster, multi-site federation similar to AWS regions and tiers. Using MinIO Information Lifecycle Management (ILM), you can configure data to be tiered between NVMe based hot storage, and HDD based warm storage. All data is encrypted with per-object key. Access Control and Identity Management between the tenants are managed by MinIO using OpenID Connect or Kerberos/LDAP/AD.
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## **2. Prerequisites**
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- Install Hortonworks Distribution using this [guide.](https://docs.hortonworks.com/HDPDocuments/Ambari-2.7.1.0/bk_ambari-installation/content/ch_Installing_Ambari.html)
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- [Setup Ambari](https://docs.hortonworks.com/HDPDocuments/Ambari-2.7.1.0/bk_ambari-installation/content/set_up_the_ambari_server.html) which automatically sets up YARN
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- [Installing Spark](https://docs.hortonworks.com/HDPDocuments/HDP3/HDP-3.0.1/installing-spark/content/installing_spark.html)
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- Install MinIO Distributed Server using one of the guides below.
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- [Deployment based on Kubernetes](https://docs.min.io/docs/deploy-minio-on-kubernetes.html#minio-distributed-server-deployment)
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- [Deployment based on MinIO Helm Chart](https://github.com/helm/charts/tree/master/stable/minio)
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## **3. Configure Hadoop, Spark, Hive to use MinIO**
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After successful installation navigate to the Ambari UI `http://<ambari-server>:8080/` and login using the default credentials: [**_username: admin, password: admin_**]
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### **3.1 Configure Hadoop**
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Navigate to **Services** -> **HDFS** -> **CONFIGS** -> **ADVANCED** as shown below
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Navigate to **Custom core-site** to configure MinIO parameters for `_s3a_` connector
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```
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sudo pip install yq
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alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
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```
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Let's take for example a set of 12 compute nodes with an aggregate memory of _1.2TiB_, we need to do following settings for optimal results. Add the following optimal entries for _core-site.xml_ to configure _s3a_ with **MinIO**. Most important options here are
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```
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cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "mapred"
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mapred.maxthreads.generate.mapoutput=2 # Num threads to write map outputs
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mapred.maxthreads.partition.closer=0 # Asynchronous map flushers
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mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
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mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
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mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
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mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
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mapreduce.reduce.speculative=false # Disable speculation for reducing
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mapreduce.task.io.sort.factor=999 # Threshold before writing to disk
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mapreduce.task.sort.spill.percent=0.9 # Minimum % before spilling to disk
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```
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S3A is the connector to use S3 and other S3-compatible object stores such as MinIO. MapReduce workloads typically interact with object stores in the same way they do with HDFS. These workloads rely on HDFS atomic rename functionality to complete writing data to the datastore. Object storage operations are atomic by nature and they do not require/implement rename API. The default S3A committer emulates renames through copy and delete APIs. This interaction pattern causes significant loss of performance because of the write amplification. _Netflix_, for example, developed two new staging committers - the Directory staging committer and the Partitioned staging committer - to take full advantage of native object storage operations. These committers do not require rename operation. The two staging committers were evaluated, along with another new addition called the Magic committer for benchmarking.
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It was found that the directory staging committer was the fastest among the three, S3A connector should be configured with the following parameters for optimal results:
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```
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cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "s3a"
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fs.s3a.access.key=minio
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fs.s3a.secret.key=minio123
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fs.s3a.path.style.access=true
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fs.s3a.block.size=512M
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fs.s3a.buffer.dir=${hadoop.tmp.dir}/s3a
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fs.s3a.committer.magic.enabled=false
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fs.s3a.committer.name=directory
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fs.s3a.committer.staging.abort.pending.uploads=true
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fs.s3a.committer.staging.conflict-mode=append
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fs.s3a.committer.staging.tmp.path=/tmp/staging
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fs.s3a.committer.staging.unique-filenames=true
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fs.s3a.connection.establish.timeout=5000
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fs.s3a.connection.ssl.enabled=false
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fs.s3a.connection.timeout=200000
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fs.s3a.endpoint=http://minio:9000
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fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem
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fs.s3a.committer.threads=2048 # Number of threads writing to MinIO
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fs.s3a.connection.maximum=8192 # Maximum number of concurrent conns
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fs.s3a.fast.upload.active.blocks=2048 # Number of parallel uploads
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fs.s3a.fast.upload.buffer=disk # Use disk as the buffer for uploads
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fs.s3a.fast.upload=true # Turn on fast upload mode
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fs.s3a.max.total.tasks=2048 # Maximum number of parallel tasks
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fs.s3a.multipart.size=512M # Size of each multipart chunk
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fs.s3a.multipart.threshold=512M # Size before using multipart uploads
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fs.s3a.socket.recv.buffer=65536 # Read socket buffer hint
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fs.s3a.socket.send.buffer=65536 # Write socket buffer hint
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fs.s3a.threads.max=2048 # Maximum number of threads for S3A
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```
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The rest of the other optimization options are discussed in the links below
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- [https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html)
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- [https://hadoop.apache.org/docs/r3.1.1/hadoop-aws/tools/hadoop-aws/committers.html](https://hadoop.apache.org/docs/r3.1.1/hadoop-aws/tools/hadoop-aws/committers.html)
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Once the config changes are applied, proceed to restart **Hadoop** services.
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### **3.2 Configure Spark2**
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Navigate to **Services** -> **Spark2** -> **CONFIGS** as shown below
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Navigate to “**Custom spark-defaults**” to configure MinIO parameters for `_s3a_` connector
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Add the following optimal entries for _spark-defaults.conf_ to configure Spark with **MinIO**.
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```
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spark.hadoop.fs.s3a.access.key minio
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spark.hadoop.fs.s3a.secret.key minio123
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spark.hadoop.fs.s3a.path.style.access true
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spark.hadoop.fs.s3a.block.size 512M
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spark.hadoop.fs.s3a.buffer.dir ${hadoop.tmp.dir}/s3a
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spark.hadoop.fs.s3a.committer.magic.enabled false
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spark.hadoop.fs.s3a.committer.name directory
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spark.hadoop.fs.s3a.committer.staging.abort.pending.uploads true
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spark.hadoop.fs.s3a.committer.staging.conflict-mode append
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spark.hadoop.fs.s3a.committer.staging.tmp.path /tmp/staging
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spark.hadoop.fs.s3a.committer.staging.unique-filenames true
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spark.hadoop.fs.s3a.committer.threads 2048 # number of threads writing to MinIO
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spark.hadoop.fs.s3a.connection.establish.timeout 5000
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spark.hadoop.fs.s3a.connection.maximum 8192 # maximum number of concurrent conns
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spark.hadoop.fs.s3a.connection.ssl.enabled false
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spark.hadoop.fs.s3a.connection.timeout 200000
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spark.hadoop.fs.s3a.endpoint http://minio:9000
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spark.hadoop.fs.s3a.fast.upload.active.blocks 2048 # number of parallel uploads
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spark.hadoop.fs.s3a.fast.upload.buffer disk # use disk as the buffer for uploads
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spark.hadoop.fs.s3a.fast.upload true # turn on fast upload mode
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spark.hadoop.fs.s3a.impl org.apache.hadoop.spark.hadoop.fs.s3a.S3AFileSystem
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spark.hadoop.fs.s3a.max.total.tasks 2048 # maximum number of parallel tasks
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spark.hadoop.fs.s3a.multipart.size 512M # size of each multipart chunk
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spark.hadoop.fs.s3a.multipart.threshold 512M # size before using multipart uploads
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spark.hadoop.fs.s3a.socket.recv.buffer 65536 # read socket buffer hint
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spark.hadoop.fs.s3a.socket.send.buffer 65536 # write socket buffer hint
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spark.hadoop.fs.s3a.threads.max 2048 # maximum number of threads for S3A
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```
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Once the config changes are applied, proceed to restart **Spark** services.
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### **3.3 Configure Hive**
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Navigate to **Services** -> **Hive** -> **CONFIGS**-> **ADVANCED** as shown below
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Navigate to “**Custom hive-site**” to configure MinIO parameters for `_s3a_` connector
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Add the following optimal entries for `hive-site.xml` to configure Hive with **MinIO**.
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```
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hive.blobstore.use.blobstore.as.scratchdir=true
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hive.exec.input.listing.max.threads=50
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hive.load.dynamic.partitions.thread=25
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hive.metastore.fshandler.threads=50
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hive.mv.files.threads=40
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mapreduce.input.fileinputformat.list-status.num-threads=50
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```
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For more information about these options please visit [https://www.cloudera.com/documentation/enterprise/5-11-x/topics/admin_hive_on_s3_tuning.html](https://www.cloudera.com/documentation/enterprise/5-11-x/topics/admin_hive_on_s3_tuning.html)
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Once the config changes are applied, proceed to restart all Hive services.
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## **4. Run Sample Applications**
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After installing Hive, Hadoop and Spark successfully, we can now proceed to run some sample applications to see if they are configured appropriately. We can use Spark Pi and Spark WordCount programs to validate our Spark installation. We can also explore how to run Spark jobs from the command line and Spark shell.
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### **4.1 Spark Pi**
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Test the Spark installation by running the following compute intensive example, which calculates pi by “throwing darts” at a circle. The program generates points in the unit square ((0,0) to (1,1)) and counts how many points fall within the unit circle within the square. The result approximates pi.
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Follow these steps to run the Spark Pi example:
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- Login as user **‘spark’**.
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- When the job runs, the library can now use **MinIO** during intermediate processing.
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- Navigate to a node with the Spark client and access the spark2-client directory:
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```
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cd /usr/hdp/current/spark2-client
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su spark
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```
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- Run the Apache Spark Pi job in yarn-client mode, using code from **org.apache.spark**:
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```
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./bin/spark-submit --class org.apache.spark.examples.SparkPi \
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--master yarn-client \
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--num-executors 1 \
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--driver-memory 512m \
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--executor-memory 512m \
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--executor-cores 1 \
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examples/jars/spark-examples*.jar 10
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```
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The job should produce an output as shown below. Note the value of pi in the output.
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```
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17/03/22 23:21:10 INFO DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 1.302805 s
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Pi is roughly 3.1445191445191445
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```
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Job status can also be viewed in a browser by navigating to the YARN ResourceManager Web UI and clicking on job history server information.
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### **4.2 WordCount**
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WordCount is a simple program that counts how often a word occurs in a text file. The code builds a dataset of (String, Int) pairs called counts, and saves the dataset to a file.
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The following example submits WordCount code to the Scala shell. Select an input file for the Spark WordCount example. We can use any text file as input.
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- Login as user **‘spark’**.
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- When the job runs, the library can now use **MinIO** during intermediate processing.
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- Navigate to a node with Spark client and access the spark2-client directory:
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```
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cd /usr/hdp/current/spark2-client
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su spark
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```
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The following example uses _log4j.properties_ as the input file:
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#### **4.2.1 Upload the input file to HDFS:**
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```
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hadoop fs -copyFromLocal /etc/hadoop/conf/log4j.properties
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s3a://testbucket/testdata
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```
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#### **4.2.2 Run the Spark shell:**
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```
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./bin/spark-shell --master yarn-client --driver-memory 512m --executor-memory 512m
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```
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The command should produce an output as shown below. (with additional status messages):
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```
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Spark context Web UI available at http://172.26.236.247:4041
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Spark context available as 'sc' (master = yarn, app id = application_1490217230866_0002).
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Spark session available as 'spark'.
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Welcome to
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____ __
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/ __/__ ___ _____/ /__
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_\ \/ _ \/ _ `/ __/ '_/
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/___/ .__/\_,_/_/ /_/\_\ version 2.1.0.2.6.0.0-598
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/_/
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Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_112)
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Type in expressions to have them evaluated.
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Type :help for more information.
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scala>
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```
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- At the _scala>_ prompt, submit the job by typing the following commands, Replace node names, file name, and file location with your values:
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```
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scala> val file = sc.textFile("s3a://testbucket/testdata")
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file: org.apache.spark.rdd.RDD[String] = s3a://testbucket/testdata MapPartitionsRDD[1] at textFile at <console>:24
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scala> val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
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counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25
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scala> counts.saveAsTextFile("s3a://testbucket/wordcount")
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```
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Use one of the following approaches to view job output:
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View output in the Scala shell:
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```
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scala> counts.count()
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364
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```
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To view the output from MinIO exit the Scala shell. View WordCount job status:
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```
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hadoop fs -ls s3a://testbucket/wordcount
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```
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The output should be similar to the following:
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```
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Found 3 items
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-rw-rw-rw- 1 spark spark 0 2019-05-04 01:36 s3a://testbucket/wordcount/_SUCCESS
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-rw-rw-rw- 1 spark spark 4956 2019-05-04 01:36 s3a://testbucket/wordcount/part-00000
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-rw-rw-rw- 1 spark spark 5616 2019-05-04 01:36 s3a://testbucket/wordcount/part-00001
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```
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